PROJECT SUMMARY Rapid adaptation of viral and microbial populations underlies a number of the most pressing human health concerns, such as viral outbreak, microbial dysbiosis, and tumor progression. In wild metazoan populations substantial phenotypic shifts have been observed in just tens of generations as well, prompting questions about our potential adaptive response to dramatic ongoing ecological and environmental change. Yet the mechanisms and dynamics of rapid adaptation in natural settings for large metazoan populations are poorly defined. Specifically lacking is a high-resolution, high-powered account of the genomic drivers of rapid adaptation, and the selective strength required to generate observed genomic shifts. Our over-arching goal is to define the genomic landscape of rapid adaptation in sexual eukaryotes, and illuminate the genomic factors that enable or prevent adaptive change. Advances in massively parallel sequencing techniques have yielded new experimental techniques in which adaptive genomic shifts can be assayed in real-time. Evolve-and-resequence (E+R) approaches, involving repeated genome-scale sequencing of large evolving populations, offer a promising window into the genes involved in rapid adaptation and the selective forces shaping adaptive dynamics. A current obstacle however is that E+R schemes have been applied and validated only sparingly, with limited knowledge of the expected resolution, precision, and signal-to-noise ratio, especially in natural environmental settings. Here we leverage data from empirical assays of rapid adaptation in large populations of a well-characterized model organism, Drosophila melanogaster, in order to define optimal and robust experimental and analytic frameworks for E+R. Our immediate objectives are 1) to develop a statistical basis for designing E+R schemes, incorporating tradeoffs in power and precision in light of linkage disequilibrium, and 2) identify a cross-validated set of SNPs subject to strong seasonal selection in Drosophila, which we will use to define the genomic architecture associated with rapid adaptation. To do so, we will complement evolutionary simulations with the analysis of multiple large-scale empirically-derived datasets, and use power analysis and advanced likelihood models to increase both the precision of allele frequency measurements and the detection resolution of selected alleles. Our work stands to maximize the power of promising experimental efforts and expedite the translation of raw genomic data to true biological insight. Our results will begin the transition of rapid adaptation studies from observational to predictive, informing efforts to combat the aggressive selective shifts accompanying disease.